Artificial intelligence, machine learning, deep learning and beyond

Understanding AI technologies and how they lead to smart applications

By Wayne Thompson, Hui Li and Alison Bolen

Artificial intelligence (AI) brings with it a promise of genuine human-to-machine interaction. When machines become intelligent, they can understand requests, connect data points and draw conclusions. They can reason, observe and plan. Consider:

Clearly, we’re not talking about robotic butlers. This isn’t a Hollywood movie. But we are at a new level of cognition in the artificial intelligence field that has grown to be truly useful in our lives.

We get it, though. You’re still confused about how all these topics – AI, machine learning and deep learning – relate. You’re not alone. And we want to help.

In this article we’ll explore the basic components of artificial intelligence and describe how various technologies have combined to help machines become more intelligent.

AI computing advances explained

What are the building blocks of AI? And how can computers interact and communicate with humans? Find out in this informative webinar.

The history of AI and machine learning

So where did AI come from? Well, it didn’t leap from single-player chess games straight into self-driving cars. The field has a long history rooted in military science and statistics, with contributions from philosophy, psychology, math and cognitive science. Artificial intelligence originally set out to make computers more useful and more capable of independent reasoning.

Most historians trace the birth of AI to a Dartmouth research project in 1956 that explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and increased the focus on training computers to mimic human reasoning.

For example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Google, Amazon or Microsoft tackled similar projects.

This work paved the way for the automation and formal reasoning that we see in computers today.

Where to Start with AI and Machine Learning

What kind of problem are you trying to solve? And what kind of data do you have? asks Kirk Borne, PhD, Principal Data Scientist at Booz Allen Hamilton. "Think big but start small," is part of his philosophy for getting started with machine learning.

Machine learning and deep learning are subfields of AI

As a whole, artificial intelligence contains many subfields, including:

Machine learningautomates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without being explicitly programmed where to look or what to conclude.

A neural network is a kind of machine learning inspired by the workings of the human brain. It’s a computing system made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.

Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.

Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.

Natural language processing is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.

While machine learning is based on the idea that machines should be able to learn and adapt through experience, AI refers to a broader idea where machines can execute tasks "smartly."

How big data plus AI produced smart applications

Remember the big data hoopla a few years ago? What was that about? Advancements in computer processing and data storage made it possible to ingest and analyze more data than ever before. Around the same time, we started producing more and more data by connecting more devices and machines to the internet and streaming large amounts of data from those devices.

With more language and image inputs into our devices, computer speech and image recognition improved. Likewise, machine learning had much more information to learn from.

All of these advancements brought artificial intelligence closer to its original goal of creating intelligent machines, which we're starting to see more and more in our everyday lives. From recommendations on our favorite retail sites to auto generated photo tags on social media, many common online conveniences are powered by artificial intelligence.

Real-world benefits of
artificial intelligence

In health care, treatment effectiveness can be more quickly determined. In retail, add-on items can be more quickly suggested. In finance, fraud can be prevented instead of just detected.

Where are we today with AI?

With AI, you can ask a machine questions – out loud – and get answers about sales, inventory, customer retention, fraud detection and much more. The computer can also discover information that you never thought to ask. It will offer a narrative summary of your data and suggest other ways to analyze it. It will also share information related to previous questions from you or anyone else who asked similar questions. You’ll get the answers on a screen or just conversationally.

How will this play out in the real world? In health care, treatment effectiveness can be more quickly determined. In retail, add-on items can be more quickly suggested. In finance, fraud can be prevented instead of just detected. And so much more.

In each of these examples, the machine understands what information is needed, looks at relationships between all the variables, formulates an answer – and automatically communicates it to you with options for follow-up queries.

We have decades of artificial intelligence research to thank for where we are today. And we have decades of intelligent human-to-machine interactions to come.

About the Authors

Wayne Thompson is the manager of data science technologies at SAS. He is one of the early pioneers of business predictive analytics, and he is a globally recognized presenter, teacher, practitioner, and innovator in the field of predictive analytics technology.

Hui Li is a senior staff scientist at SAS. She has 10 years of experience in machine learning, data mining, data analysis and statistical Modeling, plus 15 years of experience in C programming and hybrid C/Matlab programming.

Alison Bolen is an editor at SAS, where she writes content about analytics and emerging technologies. She makes it a daily goal to simplify and explain complex subjects.